Selego: robust variate selection for accurate time series forecasting

Naïve extensions of uni-variate prediction techniques lead to an unwelcome increase in the cost of multi-variate model learning and significant deteriorations in the model performance. In this paper, we first argue that (a) one can learn a more accurate forecasting model by leveraging temporal alignments among variates to quantify the importance of the recorded variates with respect to a target variate. We further argue that, (b) for this purpose we need to quantify temporal correlation, not in terms of series similarity, but in terms of temporal alignments of key “events” impacting these series. Finally, we argue that (c) while learning a temporal model using recurrence based techniques (such as RNN and LSTM—even when leveraging attention strategies) is difficult and costly, we can achieve better performance by coupling simpler CNNs with an adaptive variate selection strategy. Relying on these arguments, we propose a Selego framework (Selego is a word of latin origin meaning “selection”) for variate selection and experimentally evaluate the performance of the proposed approach on various forecasting models, such as LSTM, RNN, and CNN, for different top-X% variates and different forecasting time in the future (lead) on multiple real-world datasets. Experiments show that the proposed framework can offer significant (90 - 98 %) drops in the number of recorded variates that are needed to train predictive models, while simultaneously boosting accuracy.